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Estimation of Brain Deformation for Volumetric Image Updating in Protoporphyrin IX Fluorescence-Guided ResectionValdés P.A.a, b · Fan X.b · Ji S.b · Harris B.T.a, c · Paulsen K.D.a, b, d · Roberts D.W.a, d, e
aDartmouth Medical School and bThayer School of Engineering, Dartmouth College, Hanover, N.H., cDepartment of Pathology, dNorris Cotton Cancer Center, and eSection of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, N.H., USA Corresponding Author
David W. Roberts, MD
Section of Neurosurgery, Dartmouth-Hitchcock Medical Center
One Medical Center Drive
Lebanon, NH 03756 (USA)
Tel. +1 603 650 8734, Fax +1 603 650 7911, E-Mail David.W.Roberts@dartmouth.edu
Introduction: Fluorescence-guided resection (FGR) of brain tumors is an intuitive, practical and emerging technology for visually delineating neoplastic tissue exposed intraoperatively. Image guidance is the standard technique for producing 3-dimensional spatially coregistered information for surgical decision making. Both technologies together are synergistic: the former detects surface fluorescence as a biomarker of the current surgical margin while the latter shows coregistered volumetric neuroanatomy but can be degraded by intraoperative brain shift. We present the implementation of deformation modeling for brain shift compensation in protoporphyrin IX FGR, integrating these two sources of information for maximum surgical benefit. Methods: Two patients underwent FGR coregistered with conventional image guidance. Histopathological analysis, intraoperative fluorescence and image space coordinates were recorded for biopsy specimens acquired during surgery. A biomechanical brain deformation model driven by intraoperative ultrasound data was used to generate updated MR images. Results: Combined use of fluorescence signatures and updated MR image information showed substantially improved accuracy compared to fluorescence or the original (i.e., nonupdated) MR images, detecting only true positives and true negatives, and no instances of false positives or false negatives. Conclusion: Implementation of brain deformation modeling in FGR shows promise for increasing the accuracy of neurosurgical guidance in the delineation and resection of brain tumors.
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